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2,453 results for “Architecture”
[opendc-sc18-dataset] A Reference Architecture for Datacenter Scheduling: Data Artifacts
<p>This release contains the data artifacts of the paper A Reference Architecture for Datacenter Scheduling presented at <a href="https://sc18.supercomputing.org/">Supercomputing 2018</a></p> <p>For the paper, experiments have been run on the following traces:</p> <ul> <li><strong>Askalon (W-Eng)</strong> - <code>askalon_workload_ee</code></li> <li><strong>Chronos (W-Ind)</strong> - <code>chronos_exp_noscaler_ca</code></li> </ul> <p>Each of the directories for the traces have the following structure:</p> <ul> <li><strong>/setup.txt</strong><br> This text file describes the trace used for the experiment in addition to the amount of times the experiment was repeated and the amount of warm-up experiments.</li> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter used in the experiments. Each item represents the identifiers of the resource (here, CPU type) to use in the machine. The available CPU types are (1) Intel i7 (4 cores, 4100 MHz) and (2) Intel i5 (2 cores, 3500 MHz).</li> <li><strong>/trace</strong><br> This directory contains the trace used in the simulation. The trace is stored in the Grid Workload Format. See the <a href="http://gwa.ewi.tudelft.nl/">Grid Workload Archive</a> for more information.</li> <li><strong>/data/experiments.csv</strong><br> A CSV file containing information of all simulations that have been run on the OpenDC platform for this experiment.</li> <li><strong>/data/job_metrics.csv</strong><br> A CSV file containing metrics (NSL, JMS, etc.) for each job that ran during the simulations.</li> <li><strong>/data/stage_measurements.csv</strong><br> A CSV file containing timing measurements for the scheduling stages that ran during the simulations.</li> <li><strong>/data/task_metrics.csv</strong><br> A CSV file containing metrics for each task that ran during the simulations.</li> <li> <p><strong>/data/tasks.csv</strong><br> A CSV file containing information about the tasks (submit time, runtime, etc.) that ran during the simulations as extracted from the traces.</p> <p>Additionally, we describe the format of each data file in the associated metadata file.</p> </li> </ul> <p><strong>Hardware</strong></p> <p>The hardware used for running the experiments is a MacBook Pro with a 2,9 GHz Intel Core i7 processor and 16 GB 2133 MHz LPDDR3 internal memory.</p> <p><strong>Reproduction</strong></p> <p>This section describes the instructions for reproducing the paper results using a provided Docker image. Please make sure you have <a href="https://www.docker.com/">Docker</a> installed and running.</p> <p>For reproduction, you will run the following experiments:</p> <ul> <li><code>askalon_workload_ee</code><br> This is the large experiment of the paper and will take approximately 4 hours to complete similar hardware.</li> <li><code>chronos_exp_noscaler_ca</code><br> This is the smaller experiment of the paper and will take approximately 5 minutes to complete on similar hardware.</li> </ul> <p>The Docker image <a href="https://hub.docker.com/r/atlargeresearch/sc18-experiment-runner/"><code>atlargeresearch/sc18-experiment-runner</code></a> can be used for running the experiments. A volume can be attached to the directory <code>/home/gradle/simulator/data</code> to capture the results of the experiments.</p> <p>Make sure you have, in your current working directory, the following files:</p> <ul> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter and can be found in this archive at <code>askalon_workload_ee/setup.json</code>.</li> <li><strong>/askalon_workload_ee.gwf</strong><br> This file contains the trace for the Askalon workload. This file can be found in the archive at <code>askalon_workload_ee/trace/askalon_workload_ee.gwf</code>.</li> <li><strong>/chronos_exp_noscaler_ca.gwf</strong><br> This file contains the trace for the Chronos workload. This file can be found in the archive at <code>chronos_exp_noscaler_ca/trace/chronos_exp_noscaler_ca.gwf</code>.</li> </ul> <p>Then, you can start the Askalon experiments as follows:</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/askalon_workload_ee.gwf </code></pre> <p>The experiment runner can be configured with the following options</p> <ul> <li><strong>-r</strong>, <strong>--repeat</strong><br> The amount of times to repeat an experiment for each scheduler.</li> <li><strong>-w</strong>, <strong>--warm-up</strong><br> The amount of times to warm-up the simulator for each scheduler.</li> <li><strong>-p</strong>, <strong>--parallelism</strong><br> The number of experiments to run in parallel.</li> <li><strong>--schedulers</strong><br> The list of schedulers to test, separated by spaces. The following schedulers are available: <code>SRTF-BESTFIT</code>, <code>SRTF-FIRSTFIT</code>, <code>SRTF-WORSTFIT</code>, <code>FIFO-BESTFIT</code>, <code>FIFO-FIRSTFIT</code>, <code>FIFO-WORSTFIT</code>, <code>RANDOM-BESTFIT</code>, <code>RANDOM-FIRSTFIT</code>, <code>RANDOM-WORSTFIT</code>.</li> </ul> <p>After the Askalon experiments have been finished, you can start the Chronos experiments. <strong>Make sure</strong> you have a copy of the result files in your directory as the result files will be overwritten.</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/chronos_exp_noscaler_ca.gwf </code></pre>
Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1
<p>This repository contains the modeling files and the analysis related to the article <a href="https://www.ncbi.nlm.nih.gov/pubmed/30190596">"Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1"</a> by Jishage et al. in Nat Struct Mol Biol 2018.</p> <p><strong>For more information</strong> about how to reproduce this modeling, see the <a href="https://salilab.org/pol_ii_g/">Sali lab website</a> or the README file.</p>
Identification of novel genes involved in phosphate accumulation in Lotus japonicus through Genome Wide Association mapping of root system architecture and anion content
<p>130 Lotus japonicus accessions were used. The names and accession numbers are<br> listed in S6 Table. Seeds were scarified with sandpaper and then sterilized 14 minutes in 0.05%<br> sodium hypochlorite. Subsequently, seeds were rinsed and washed 5 times in sterile distilled<br> water. For the germination, seeds were positioned in imbibed filter paper, in sterile Petri dishes,<br> and wrapped in aluminium foil. After 3 days at 21°C, young seedling were transferred to square<br> plates (12 x 12 cm) containing growth medium. Both media used in this<br> study were based on Long-Ashton solution (with two levels of phosphate concentration -20 or<br> 750 μM, LP or HP, respectively) with 0.8% MES buffer (Duchefa Biochemie,<br> Haarlem, The Netherlands), 0.8% agarose (to minimize phosphate contamination), and adjusted<br> to pH 5.7 with 1M KOH. After adding the medium, plates were dried, closed, overnight in a<br> sterile laminar flow hood. Two accessions, with four replicates per each accession, were placed<br> on each plate. Each plate was replicated, with mirrored position of each accession to minimize<br> any positional growth effects. Plates were placed vertically, and plants grown under long-day<br> conditions (21°C, 16 h light/8 h dark cycle) with white light bulbs emitting 50 μmol/m 2 /s and<br> roots were exposed to light. Every day at the same time, the racks were transported to the image<br> acquisition room where images of each plate were acquired with eight Epson V600 CCD flatbed<br> color image scanners (Seiko Epson) and then immediately returned to the growth chamber.</p>
Architecture of the healthcare ecosystem for caregivers in C4 Model
<p>The healthcare ecosystem for caregivers is focused on supporting the learning and knowledge management processes to develop and enhance the caregiving competences both at home and in care environments of formal and informal caregivers (García-Holgado, Marcos-Pablos & García-Peñalvo, 2019).</p> <p>This model is part of a paper accepted in UCAmI2019: 13th International Conference on Ubiquitous Computing and Ambient Intelligence (Vázquez-Ingelmo, García-Holgado, García-Peñalvo & Therón, 2019).</p> <p> </p> <p>García-Holgado, A., Marcos-Pablos, S., & García-Peñalvo, F. J. (2019). A Model to Define an eHealth Technological Ecosystem for Caregivers. In Á. Rocha, H. Adeli, L. Reis, & S. Costanzo (Eds.), New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing (Vol. 932, pp. 422-432). Cham: Springer.</p> <p>Vázquez-Ingelmo, A., García-Holgado, A., García-Peñalvo, F. J., & Therón, R. (2019). Dashboard metamodel for knowledge management in technological ecosystem: a case study in healthcare. In press.</p>
CHAS - Cultural Heritage Architectural Segmentation dataset
<p>CHAS is a point cloud dataset from cultural heritage aimed to provide data for semantic segmentation techniques. The data in this repository were generated by terrestrial laser scanning and pictures from aerial survey using UAV. This dataset comprises relevant buildings representing religious and colonial Brazilian architecture. We hope to allow future research in point cloud segmentation and automatic Building Information Modeling.</p> <p>We use the .7z file format to compact the clouds, providing high compression ratio. File names with "raw" suffix refers to RGB registered cloud. The "gt" suffix stands for ground truth clouds.</p> <p> </p> <p><strong>Raw point cloud description</strong></p> <p>Name Architectural Style Construction Year TLS UAV Points* ID</p> <p>Good Death's Church Neoclassical 1867 Yes Yes 210 <em>boa_morte</em><br> Central Mill Industrial 1881 Yes Yes 26 <em>engenho</em><br> Ball House Modern 1940 Yes No 370 <em>baile</em><br> Quilombo Farm Colonial 1891 Yes Yes 55 <em>quilombo</em> St. Francis of Assisi Church Modern 1943 Yes Yes 46 <em>curia</em></p> <p>* millions </p> <p> </p> <p><strong>Equipment</strong></p> <ul> <li>FARO Focus3D X 330 HDR*</li> <li>DJI Inspire 2 with Gimbal DJI Zenmuse X4S 4K | H264 | F2.8 - F11 | 20MP**</li> <li>DJI Spark 12 MP Full HD GPS **</li> </ul> <p><em>*Terrestrial Laser Scanner (TLS)</em></p> <p><em>**Unmanned Aerial Vehicle (UAV)</em></p> <p> </p> <p><strong>Software</strong></p> <p>The following third-party software have been used to register the clouds.: FARO SCENE, CloudCompare and Pix4D Mapper. For a better understanding of the hybrid point cloud registration approach used in this dataset we recommend the following article: <a href="http://papers.cumincad.org/cgi-bin/works/paper/ecaade2018_295">BIM for Heritage Documentation An ontology-based approach</a>.</p> <p> </p> <p><strong>Ground-truth</strong></p> <p>Ground-Truth in this dataset was manually created with the Interactive Segmentation Tool on CloudCompare. Distinct colors represent different architectural elements. An “optimal” segmentation algorithm must partition the raw point clouds into equivalent single-color ground-truth clusters. </p> <p> </p> <p><strong>How to cite this dataset?</strong></p> <p>Paiva, P. V. V., Cogima, C. K., Dezen-Kempter, E. and Carvalho, M. A. G. "Historical building point cloud segmentation combining hierarchical watershed transform and curvature analysis." <em>Pattern Recognition Letters</em> 135 (2020): 114-121. DOI: <a href="https://doi.org/10.1016/j.patrec.2020.04.010">https://doi.org/10.1016/j.patrec.2020.04.010</a></p> <p>Paiva, P. V. V., Cogima, C. K., Dezen-Kempter, E. and Carvalho, M. A. G. "<em>CHAS - Cultural Heritage Architectural Segmentation dataset</em>". Zenodo, March 26 2019. DOI: <a href="https://doi.org/10.5281/zenodo.2609498">https://doi.org/10.5281/zenodo.2609498</a></p> <p> </p>
Fig. 6 in Elbella luteizona (Mabille, 1877) (Lepidoptera, Hesperiidae: Pyrginae) in Brazilian Cerrado: larval morphology, diet, and shelter architecture
Fig. 6. Elbella luteizona, scanning electron microscopy of egg. (A) Dorso-lateral view, Mp = micropylar region; (B) horizontal and vertical carinae forming cells, lateral view; (C) basal region, with incomplete carinae, lateral view; (D) micropylar region, dorsal view; (E) arrow indicating aeropyles (Ae), dorsal view.
Fig. 1 in Elbella luteizona (Mabille, 1877) (Lepidoptera, Hesperiidae: Pyrginae) in Brazilian Cerrado: larval morphology, diet, and shelter architecture
Fig. 1. Elbella luteizona host plants in the Cerrado, Distrito Federal, Brazil. (A) Byrsonima coccolobifolia; (B) Myrsine guianensis.
Text-fig. 11. Eospondylus cf. primigenius (STÜRTZ) "Prastav" quarry at Praha-Holyně, Třebotov Limestone, Lower Devonian, Dalejan, NM L 36905, x 65. Overlay of proximal and distal articulations. Upper photo is proximal surface with distal bird-like articulation knobs superposed in ink. Lower photo is distal surface with proximal articulation knobs superposed in ink. The architecture of articulation surfaces is both zygospondylous and auluroid. This architecture occurs also in vertebrae of Furcaster and indicates that families Eospondylidae and Furcasteridae are closely related. in Isolated Ossicles Of The Family Eospondylidae Spencer Wright, 1966, In The Lower Devonian Of Bohemia (Czech Republic) And Correction Of The Systematic Position Of Eospondylid Brittlestars (Echinodermata: Ophiuroidea: Oegophiurida)
Text-fig. 11. Eospondylus cf. primigenius (STÜRTZ) "Prastav" quarry at Praha-Holyně, Třebotov Limestone, Lower Devonian, Dalejan, NM L 36905, x 65. Overlay of proximal and distal articulations. Upper photo is proximal surface with distal bird-like articulation knobs superposed in ink. Lower photo is distal surface with proximal articulation knobs superposed in ink. The architecture of articulation surfaces is both zygospondylous and auluroid. This architecture occurs also in vertebrae of Furcaster and indicates that families Eospondylidae and Furcasteridae are closely related.
Figure 1 in Notes on the nest architecture of Centris (Centris) caxiensis Ducke (Hymenoptera: Apidae) in an urban dry forest fragment in northeastern Brazil
Figure 1. Main characteristics of the nesting habits of Centris caxiensis. (a) General view of nest site; (b) Excavated nest; (c) Females cleaning nest entrance; (d) Females digging the nest (arrow 1: brood cell; arrow 2: nest entrance).
Figure 2 in Notes on the nest architecture of Centris (Centris) caxiensis Ducke (Hymenoptera: Apidae) in an urban dry forest fragment in northeastern Brazil
Figure 2. Nest features of Centris caxiensis. (a) Nest with a single brood cell; (b), Unfinished nest; (c) Nest with two brood cells; (d) Brood cell: 1-sand filling tunnel; 2- nest entrance; 3-curvature in the tunnel; 4-unfinished cell; 5- central process.
Figure 2 3D in A 3D model to illustrate the nest architecture of Acromyrmex balzani (Hymenoptera; Formicidae)
Figure 2 3D profile of nests 5 to 8, showing turret height and maximum depth. a: nest 5; b: nest 6; c: nest 7 and d: nest 8.
Figure 3 in A 3D model to illustrate the nest architecture of Acromyrmex balzani (Hymenoptera; Formicidae)
Figure 3 Pearson's Correlation between the number of workers and the number of chambers (a); between the number of workers and the nest volume (b) and between the number of chambers and the nest volume (c), with their respective r and p values.
Fig. 2 in Nest architecture development of grass-cutting ants
Fig. 2. Detail of the fungal chamber of Atta bisphaerica in different forms (A and B). Botucatu, SP, 2014.
Fig. 1 in Effect of the presence of brood and fungus on the nest architecture and digging activity of Acromyrmex subterraneus Forel (Hymenoptera, Formicidae)
Fig. 1. Plaster mold of a nest excavated by Acromyrmexsubterraneus workers. (A) Plaster mold of tunnels; (B) a molded and dried nest ready to be removed; (C) labeled and measured structure.
Fig. 3 in Effect of the presence of brood and fungus on the nest architecture and digging activity of Acromyrmex subterraneus Forel (Hymenoptera, Formicidae)
Fig. 3. Boxplot showing the variation in digging activity according to treatment (indicated above each graph) and time.
Figure 2 in Exploring Energy Management on GPUs in Game Architectures
Figure 2. - Photographic evidence from New Caledonia. (A) Inter-specific depredation of a ~1.2 m TL grey reef shark C. amblyrhynchos (GRS) that was depredat- ed by a ~2.6 m TL bullshark C. leucas (BS), which was itself almost entirely consumed probably by a larger conspecific after (B) consuming the grey reef shark (Photos courtesy of B. Claude).
Figure 1 - Photographic evidence from Seychelles. A in Exploring Energy Management on GPUs in Game Architectures
Figure 1 - Photographic evidence from Seychelles. A: Intra-specific depredation of a ~2 m TL tiger shark Galeocerdo cuvier (TS1) by a 4 m TL conspecific (TS2); B: Inter-specific depredation based on the catch of a ~1≈m TL grey reef shark Carcharhinus amblyrhynchos (GRS), that was almost entirely consumed by C: ~2 m TL bullshark C. leucas (BS), which was (D) itself depredated, probably by a large tiger shark (Photo courtesy of JBG).
Fig. 6 in Constraints on the lamina density of laminar bone architecture of large-bodied dinosaurs and mammals
Fig. 6. Lamina density vs. femur length of sauropodomorph dinosaur taxa (Plateosaurus and neosauropods). Among the neosauropods, lamina density does not correlate with femur length, although a slight decrease may take place with increasing femur length (Slope = -0.001; Intercept = 5.61; Pearson's R = -0.372, two-tailed p = 0.052). High variability of lamina density in Plateosaurus may be related to its developmental plasticity (cf. Sander and Klein 2005).
Fig. 5 in Constraints on the lamina density of laminar bone architecture of large-bodied dinosaurs and mammals
Fig. 5. Comparison of dinosaur and mammal lamina density. A test for normality of the combined distributions failed (which is common for large datasets), but descriptive statistics suggest the dataset may still be normal (skew [lopsidedness] = 0.449; kurtosis [peakedness or flatness] = -0.107). Mean mammal lamina density differs significantly from mean dinosaur lamina density (independent t-test, t = 5.928; p <0.001). A non-parametric alternative suggests an equally significant difference between the medians (Mann-Whitney U statistic = 752.0; two-tailed p value <0.001). For discussion of these results, please refer to the main text.
Fig. 4 in Constraints on the lamina density of laminar bone architecture of large-bodied dinosaurs and mammals
Fig. 4. Comparison of the frequency distributions of lamina density of the different mammal groups. These data represent 24 of our own samples complemented with 15 elephantid samples from Curtin et al. (2012). Mammal lamina density follows a normal distribution. Descriptive statistics of mammal lamina density: mean = 4.154 laminae/mm; SD = 1.517 laminae/ mm; skew = 0.964 and kurtosis = 0.289. For further discussion please refer to the main text.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.